<h2>DESCRIPTION</h2>

<em>r.regression.line</em> calculates a linear regression from two
raster maps, according to the formula
<div class="code"><pre>
y = a + b*x
</pre></div>
where
<div class="code"><pre>
x
y
</pre></div>
represent the input raster maps.
<p>
Optionally, it saves regression coefficients as a ASCII file. 
The result includes the following coefficients:
offset/intercept (a) and gain/slope (b), correlation coefficient (R),
number of elements (N), means (medX, medY), standard deviations
(sdX, sdY), and the F test for testing the significance of the
regression model as a whole (F).

<h2>NOTES</h2>
The results for offset/intercept (a) and gain/slope (b) are 
identical to that obtained from R-stats's lm() function. 

<h2>EXAMPLE</h2>

Comparison of two DEMs (SRTM and NED, both at 30m resolution),
provided in the North Carolina sample dataset:

<div class="code"><pre>
g.region raster=elev_srtm_30m -p
r.regression.line mapx=elev_ned_30m mapy=elev_srtm_30m 
 y = a + b*x
   a (Offset): -1.659279
   b (Gain): 1.043968
   R (sumXY - sumX*sumY/N): 0.894038
   N (Number of elements): 225000
   F (F-test significance): 896093.366283
   meanX (Mean of map1): 110.307571
   sdX (Standard deviation of map1): 20.311998
   meanY (Mean of map2): 113.498292
   sdY (Standard deviation of map2): 23.718307
</pre></div>
<p>
Using the script style flag AND <em>eval</em> to make results
available in the shell:
<div class="code"><pre>
g.region raster=elev_srtm_30m -p
eval `r.regression.line -g mapx=elev_ned_30m mapy=elev_srtm_30m`

# print result stored in respective variables
echo $a
-1.659279

echo $b
1.043968

echo $R
0.894038
</pre></div>

<h2>SEE ALSO</h2>

<em>
<a href="d.correlate.html">d.correlate</a>,
<a href="r.regression.multi.html">r.regression.multi</a>,
<a href="r.stats.html">r.stats</a>
</em>


<h2>AUTHORS</h2>

Dr. Agustin Lobo - alobo at ija.csic.es<br>
Updated to GRASS 5.7 Michael Barton, Arizona State University<br>
Script style output Markus Neteler<br>
Conversion to C module Markus Metz

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